The purpose of this dissertation is to investigate the impact of malpractice liability claims on the availability and quality of care over time. There are three primary research objectives. The first objective is to test whether an individual physician's history of malpractice claims influences health outcomes and volumes of high-risk procedures for that physician, specifically in the field of obstetrics. The second objective is to understand how malpractice signals are transmitted to and across individual physicians and hospitals over time. The third goal is to analyze whether the extent to which physicians alter their patient mix in response to a threat of more malpractice claims depends on observable patient characteristics (i.e. co-morbidities, or Medicaid insurance). Findings from this research will help to inform the policy question of how malpractice changes the availability and quality of care over time. It will also address the policy question of whether deterrence of negligent care can occur even in the absence of individual experience rating. With respect to theory, this research can help to identify a more precise mechanism for understanding physician responses to malpractice claims, including defensive medicine. The approach taken in this proposal is based on the premise that a rise in malpractice claims is associated with a rise in malpractice premiums, which exacerbates financial pressure on physicians. Malpractice claims also may lead to non-financial pressure due to the unpleasantness of defending a claim or loss in reputation. The proposed work will test several hypotheses and analyze the effect of malpractice claims on health outcomes, volumes, and patient mix. One hypothesis is that a physician's mix of Medicaid patients should decrease over time in response to malpractice claims. Panel data from the Florida Closed Claims Database and the Inpatient Discharge Files from 1992-2000 will be used to achieve these objectives. This will be the first work to use this rich, new, and unique panel dataset on individual-level claims, directly linked to inpatient outcomes and volume. In all models, risk adjustment for patient severity will be included. First-differencing in regressions will be used to mitigate bias from unobserved heterogeneity, which has been a criticism of cross-sectional studies in this literature.